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Local Correlation Ensemble with GCN Based on Attention Features for Cross-domain Person Re-ID

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Published:06 February 2023Publication History
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Abstract

Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person’s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.

REFERENCES

  1. [1] Amig Enrique, Gonzalo Julio, Artiles Javier, and Verdejo Felisa. 2009. A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retriev. 12, 4 (2009), 461486.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Cao Yue, Xu Jiarui, Lin Stephen, Wei Fangyun, and Hu Han. 2019. GCNet: Non-local networks meet squeeze-excitation networks and beyond. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19) Workshops.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Chen Binghui, Deng Weihong, and Hu Jiani. 2019. Mixed high-order attention network for person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19). 371381.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Chen Yanbei, Zhu Xiatian, and Gong Shaogang. 2019. Instance-guided context rendering for cross-domain person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19).Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Chiang Wei-Lin, Liu Xuanqing, Si Si, Li Yang, Bengio Samy, and Hsieh Cho-Jui. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 257266.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Dai Yongxing, Liu Jun, Bai Yan, Tong Zekun, and Duan Ling-Yu. 2021. Dual-refinement: Joint label and feature refinement for unsupervised domain adaptive person re-identification. IEEE Trans. Image Process. 30 (2021), 78157829.Google ScholarGoogle Scholar
  7. [7] Dai Zuozhuo, Wang Guangyuan, Zhu Siyu, Yuan Weihao, and Tan Ping. 2021. Cluster contrast for unsupervised person re-identification. arXiv:2103.11568. Retrieved from https://arxiv.org/abs/2103.11568.Google ScholarGoogle Scholar
  8. [8] Deng Weijian, Zheng Liang, Ye Qixiang, Kang Guoliang, Yang Yi, and Jiao Jianbin. 2018. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Fan Hehe, Zheng Liang, Yan Chenggang, and Yang Yi. 2018. Unsupervised person re-identification: Clustering and fine-tuning. ACM Trans. Multimedia Comput. Commun. Appl. 14, 4 (2018), 118.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Fu Yang, Wei Yunchao, Wang Guanshuo, Zhou Yuqian, Shi Honghui, and Huang Thomas S.. 2019. Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19).Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Ge Yixiao, Chen Dapeng, and Li Hongsheng. 2020. Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. In Proceedings of the 8th International Conference on Learning Representations (ICLR’20).Google ScholarGoogle Scholar
  12. [12] Ge Yixiao, Zhu Feng, Chen Dapeng, Zhao Rui, and Li Hongsheng. 2020. Self-paced contrastive learning with hybrid memory for domain adaptive object re-ID. In Advances in Neural Information Processing Systems.Google ScholarGoogle Scholar
  13. [13] Hu Jie, Shen Li, and Sun Gang. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’18). 71327141.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Jiang Bo, Wang Xixi, Zheng Aihua, Tang Jin, and Luo Bin. 2021. PH-GCN: Person retrieval with part-based hierarchical graph convolutional network. IEEE Transactions on Multimedia.DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Jin Xin, Lan Cuiling, Zeng Wenjun, Chen Zhibo, and Zhang Li. 2020. Style normalization and restitution for generalizable person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Karypis George and Kumar Vipin. 1998. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20, 1 (1998), 359392.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Li Yu-Jhe, Lin Ci-Siang, Lin Yan-Bo, and Wang Yu-Chiang Frank. 2019. Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19).Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Li Yu-Jhe, Yang Fu-En, Liu Yen-Cheng, Yeh Yu-Ying, Du Xiaofei, and Wang Yu-Chiang Frank. 2018. Adaptation and re-identification network: An unsupervised deep transfer learning approach to person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18) Workshops.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Liu Chunxiao, Loy Chen Change, Gong Shaogang, and Wang Guijin. 2013. POP: Person re-identification post-rank optimisation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’13).Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Luo Hao, Jiang Wei, Gu Youzhi, Liu Fuxu, Liao Xingyu, Lai Shenqi, and Gu Jianyang. 2020. A strong baseline and batch normalization neck for deep person re-identification. IEEE Trans. Multimedia 22, 10 (2020), 25972609.Google ScholarGoogle Scholar
  21. [21] Luo Hao, Jiang Wei, Zhang Xuan, Fan Xing, Qian Jingjing, and Zhang Chi. 2019. AlignedReID++: Dynamically matching local information for person re-identification. Pattern Recogn. 94 (2019), 5361.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Mekhazni Djebril, Bhuiyan Amran, Ekladious George, and Granger Eric. 2020. Unsupervised domain adaptation in the dissimilarity space for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV’20). Springer, 159174.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Miao Jiaxu, Wu Yu, Liu Ping, Ding Yuhang, and Yang Yi. 2019. Pose-guided feature alignment for occluded person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19). 542551.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Pan Honghu, Bai Yang, He Zhenyu, and Zhang Chunkai. 2022. AAGCN: Adjacency-aware graph convolutional network for person re-identification. Knowl.-Bas. Syst. 236 (2022), 107300.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Qi Lei, Wang Lei, Huo Jing, Zhou Luping, Shi Yinghuan, and Gao Yang. 2019. A novel unsupervised camera-aware domain adaptation framework for person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19).Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Qi Xiaojuan, Liao Renjie, Jia Jiaya, Fidler Sanja, and Urtasun Raquel. 2017. 3d graph neural networks for rgbd semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’17). 51995208.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Qian Wen, He Zhiqun, Peng Silong, Chen Chen, and Wu Wei. 2021. Pseudo graph convolutional network for vehicle ReID. In Proceedings of the 29th ACM International Conference on Multimedia. 31623171.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Ristani Ergys, Solera Francesco, Zou Roger, Cucchiara Rita, and Tomasi Carlo. 2016. Performance measures and a data set for multi-target, multi-camera tracking. In Proceedings of the European Conference on Computer Vision (ECCV’16). Springer, 1735.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Rodriguez Alex and Laio Alessandro. 2014. Clustering by fast search and find of density peaks. Science 344, 6191 (2014), 14921496.Google ScholarGoogle Scholar
  30. [30] Shichao Kan, Li-hui Cen, Xinwei Zheng, YiGang Cen, Zhenmin zhu, and Wang Hengyou. 2019. A supervised learning to index model for approximate nearest neighbor image retrieval. Sign. Process.: Image Commun. 78, 10 (2019), 494502.Google ScholarGoogle Scholar
  31. [31] Shichao Kan, Linna Zhang, Zhihai He, Yigang Cen, Shiming Chen, and Zhou Jikun. 2020. Metric learning-based kernel transformer with triplets and label constraints for feature fusion. Pattern Recogn. 99, (2020), 107086.Google ScholarGoogle Scholar
  32. [32] Song Liangchen, Wang Cheng, Zhang Lefei, Du Bo, Zhang Qian, Huang Chang, and Wang Xinggang. 2020. Unsupervised domain adaptive re-identification: Theory and practice. Pattern Recogn, 102 (2020), 107173.Google ScholarGoogle Scholar
  33. [33] Sun Xiaoxiao and Zheng Liang. 2019. Dissecting person re-identification from the viewpoint of viewpoint. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19). 608617.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Sun Yifan, Zheng Liang, Yang Yi, Tian Qi, and Wang Shengjin. 2018. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proceedings of the European Conference on Computer Vision (ECCV’18). 480496.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Vinh Nguyen Xuan, Epps Julien, and Bailey James. 2010. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11 (2010), 28372854.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Wang Dongkai and Zhang Shiliang. 2020. Unsupervised person re-identification via multi-label classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Wang Jingya, Zhu Xiatian, Gong Shaogang, and Li Wei. 2018. Transferable joint attribute-identity deep learning for unsupervised person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Wei Longhui, Zhang Shiliang, Gao Wen, and Tian Qi. 2018. Person transfer GAN to bridge domain gap for person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Woo Sanghyun, Park Jongchan, Lee Joon-Young, and Kweon In So. 2018. CBAM: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV’18).Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Wu Ancong, Zheng Wei-Shi, and Lai Jian-Huang. 2019. Unsupervised person re-identification by camera-aware similarity consistency learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19). 69226931.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Wu Yu, Lin Yutian, Dong Xuanyi, Yan Yan, Bian Wei, and Yang Yi. 2019. Progressive learning for person re-identification with one example. IEEE Trans. Image Process. 28, 6 (2019), 28722881.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Xie Qiaokang, Zhou Wengang, Qi Guo-Jun, Tian Qi, and Li Houqiang. 2020. Progressive unsupervised person re-identification by tracklet association with spatio-temporal regularization. IEEE Trans. Multimedia 23 (2020), 597610.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Yang Lei, Chen Dapeng, Zhan Xiaohang, Zhao Rui, Loy Chen Change, and Lin Dahua. 2020. Learning to cluster faces via confidence and connectivity estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Yang Qize, Yu Hong Xing, Wu Ancong, and Zheng Wei Shi. 2019. Patch-based discriminative feature learning for unsupervised person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19).Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Yu Hong Xing, Zheng Wei Shi, Wu Ancong, Guo Xiaowei, Gong Shaogang, and Lai Jian Huang. 2020. Unsupervised person re-identification by soft multilabel learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).Google ScholarGoogle Scholar
  46. [46] Zhai Yunpeng, Lu Shijian, Ye Qixiang, Shan Xuebo, Chen Jie, Ji Rongrong, and Tian Yonghong. 2020. AD-cluster: Augmented discriminative clustering for domain adaptive person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Zhang Xinyu, Cao Jiewei, Shen Chunhua, and You Mingyu. 2019. Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19).Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Zhang Yue, Jin Yi, Chen Jianqiang, Kan Shichao, Cen Yigang, and Cao Qi. 2020. PGAN: Part-based nondirect coupling embedded GAN for person reidentification. IEEE MultiMedia 27, 3 (2020), 2333.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Zheng Kecheng, Lan Cuiling, Zeng Wenjun, Zhang Zhizheng, and Zha Zheng-Jun. 2021. Exploiting sample uncertainty for domain adaptive person re-identification. In Proceedings of the Association for the Advance of Artificial Intelligence (AAAI’21).Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Zheng Liang, Shen Liyue, Tian Lu, Wang Shengjin, Wang Jingdong, and Tian Qi. 2015. Scalable person re-identification: A benchmark. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’15). 11161124.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Zhong Zhun, Zheng Liang, Li Shaozi, and Yang Yi. 2018. Generalizing a person retrieval model hetero- and homogeneously. In Proceedings of the European Conference on Computer Vision (ECCV’18).Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Zhong Zhun, Zheng Liang, Luo Zhiming, Li Shaozi, and Yang Yi. 2019. Invariance matters: Exemplar memory for domain adaptive person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19). 598607.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Zhong Zhun, Zheng Liang, Luo Zhiming, Li Shaozi, and Yang Yi. 2020. Learning to adapt invariance in memory for person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 8 (2020), 2723–2738.Google ScholarGoogle Scholar

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  1. Local Correlation Ensemble with GCN Based on Attention Features for Cross-domain Person Re-ID

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 2
      March 2023
      540 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3572860
      • Editor:
      • Abdulmotaleb El Saddik
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Publication History

      • Published: 6 February 2023
      • Online AM: 9 June 2022
      • Accepted: 31 May 2022
      • Revised: 8 April 2022
      • Received: 17 December 2021
      Published in tomm Volume 19, Issue 2

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